# Uncomment the statements below if there are problems with TensorFlow on macOS
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'

## Import libraries
import pandas as pd
import numpy as np
import seaborn as sns

%matplotlib inline
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

from sklearn.metrics import (
    classification_report,
    confusion_matrix,
    accuracy_score,
    precision_recall_fscore_support,
)


import warnings
warnings.filterwarnings('ignore')

# Uncomment the statement below to allow online monitoring with TensorBoard (need to be installed)
from keras.callbacks import TensorBoard

# import tensorflow and keras
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
from tensorflow.keras.applications import MobileNetV2, VGG16
from tensorflow.keras.preprocessing.image import ImageDataGenerator

from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import MaxPool2D
from keras.models import Sequential
from keras.utils import to_categorical

import keras
print(keras.__version__)

3.10.0

print(tf.__version__)

2.19.0

IMG_SIZE        = (224, 224)
INPUT_SHAPE     = IMG_SIZE + (3,)
BATCH_SIZE      = 32
NUM_CLASSES     = 8
EPOCHS          = 100
SEED            = 42

np.random.seed(SEED)
tf.random.set_seed(SEED)
tf.config.experimental.enable_op_determinism()

classes = (
    'bumper_dent',
    'bumper_scratch',
    'door_dent',
    'door_scratch',
    'glass_shatter',
    'head_lamp',
    'tail_lamp',
    'unknown')

data_csv = "./CarDamage/data.csv"
data = pd.read_csv(data_csv)
data.head()

# Drop column "Unnamed: 0"
data.drop(['Unnamed: 0'], axis=1, inplace=True)

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1594 entries, 0 to 1593
Data columns (total 2 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   image    1594 non-null   object
 1   classes  1594 non-null   object
dtypes: object(2)
memory usage: 25.0+ KB

# Check null values
data.isnull().sum()

classes_count = data['classes'].value_counts()

# Create the barplot
plt.figure(figsize=(6, 6))
ax = sns.barplot(x=classes_count.index, y=classes_count.values)

# Add count labels on top of each bar
for i, count in enumerate(classes_count.values):
    plt.text(i, count + 0.5, str(count), ha='center', va='bottom', fontsize=10)

# Labeling
plt.xlabel('Classes')
plt.ylabel('Count')
plt.title('Class Distribution')
plt.xticks(rotation=90)

plt.tight_layout()
plt.show()

path = './CarDamage/'
def modify_image_path(x):
  return path + x

data['image'] = data['image'].apply(modify_image_path)
data.head()

fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(6, 6),  subplot_kw={'xticks': [], 'yticks': []})
for i, ax in enumerate(axes.flat):
  ax.imshow(plt.imread(data.image[i]))
  ax.set_title(data.classes[i])
plt.tight_layout()
plt.show()

train_df, test_df = train_test_split(data, test_size=0.2)

train_df.shape

(1275, 2)

test_df.shape

(319, 2)

train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=30,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=10,
    zoom_range=0.2,
    brightness_range=[0.9,1.1],
    horizontal_flip=True,
    channel_shift_range=0.1,
    fill_mode='nearest',
    validation_split=0.2
)

test_datagen = ImageDataGenerator(rescale=1./255)

train_gen = train_datagen.flow_from_dataframe(
    dataframe=train_df,
    x_col = 'image',
    y_col = 'classes',
    target_size=IMG_SIZE,
    color_mode='rgb',
    batch_size=BATCH_SIZE,
    shuffle=True,
    seed=SEED
)

val_gen = train_datagen.flow_from_dataframe(
    dataframe=train_df,
    x_col = 'image',
    y_col = 'classes',
    target_size=IMG_SIZE,
    color_mode='rgb',
    batch_size=BATCH_SIZE,
    shuffle=True,
    seed=SEED,
    subset='validation'
)

Found 1275 validated image filenames belonging to 8 classes.
Found 255 validated image filenames belonging to 8 classes.

test_gen = test_datagen.flow_from_dataframe(
    dataframe=test_df,
    x_col = 'image',
    y_col = 'classes',
    target_size=IMG_SIZE,
    color_mode='rgb',
    class_mode='categorical',
    batch_size=BATCH_SIZE,
    shuffle=False
)

Found 319 validated image filenames belonging to 8 classes.

print(type(train_gen))

<class 'keras.src.legacy.preprocessing.image.DataFrameIterator'>

images, labels = next(train_gen)

print("Images shape:", images.shape)
print("Labels shape:", labels.shape)

Images shape: (32, 224, 224, 3)
Labels shape: (32, 8)

print("Total samples:", train_gen.samples)

Total samples: 1275

images, labels = next(test_gen)
print("Images shape:", images.shape)
print("Labels shape:", labels.shape)

Images shape: (32, 224, 224, 3)
Labels shape: (32, 8)

def check_one(data, label, id = None, actual = None, compare = False):
  # check one
  if id is None:
    id = np.random.randint(data.shape[0])
  im = data[id]
  plt.figure(figsize=(3, 3))
  plt.imshow(im)

  l_id = label[id]
  #if (compare) and (actual is not None) and (l_id != np.argmax(actual[id])):
  if (compare) and (actual is not None):
    a_id = np.argmax(actual[id])
    #plt.title('Class %d (%s) [\u2260 %d-%s]' % (l_id, classes[l_id], a_id, classes[a_id]))
    plt.title(f"True: Class {a_id} - {classes[a_id]}\nPredicted: Class {l_id} - {classes[l_id]}")
  else:
    plt.title('Class %d (%s)' % (l_id, classes[l_id]))
  plt.xticks([])
  plt.yticks([])
  plt.show()

def check_ten(data, label, actual = None, compare = False):
  # check ten
  fig, ax = plt.subplots(2, 5, figsize = (16, 8))
  fig.subplots_adjust(left = 0.02, right = 0.98, top = 0.8, wspace = 0.2, hspace = 0.2)
  fig.suptitle('Check Data', fontsize = 12, fontweight = 'bold')

  plt.rcParams.update({'font.size': 10})

  ids = np.random.randint(data.shape[0], size = 10)
  r = 0
  c = 0
  for id in ids:
    im = data[id]

    # original image
    ax[r, c].imshow(im)
    l_id = label[id]
    #if (compare) and (actual is not None) and (l_id != np.argmax(actual[id])):
    if (compare) and (actual is not None):
      a_id = np.argmax(actual[id])
      #ax[r, c].set_title('Class %d (%s) [\u2260 %d-%s]' % (l_id, classes[l_id], a_id, classes[a_id]))
      ax[r, c].set_title(f"True: {classes[a_id]}\nPredicted: {classes[l_id]}")
    else:
      ax[r, c].set_title('Class %d (%s)' % (l_id, classes[l_id]))
    ax[r, c].set_xticks([])
    ax[r, c].set_yticks([])
    c += 1
    if c > 4:
      r += 1
      c = 0

  plt.show()

images, labels = next(train_gen)
check_one(images, np.argmax(labels, axis=1))

check_ten(images, np.argmax(labels, axis=1))

num_classes = len(labels[0])
print(labels[0])
print(num_classes)

[0. 0. 0. 0. 0. 1. 0. 0.]
8

def build_mobilenetv2(input_shape=INPUT_SHAPE, num_classes=NUM_CLASSES, dropout_rate=0.2):
    base = MobileNetV2(weights='imagenet', include_top=False, input_shape=input_shape)
    base.trainable = False  # freeze for initial training
    model = models.Sequential([
        base,
        layers.GlobalAveragePooling2D(),
        layers.Dropout(dropout_rate),
        layers.Dense(num_classes, activation='softmax')
    ])
    print(model.summary())
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

def build_vgg16(input_shape=INPUT_SHAPE, num_classes=NUM_CLASSES, dropout_rate=0.2):
    base = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)
    base.trainable = False
    model = models.Sequential([
        base,
        layers.Flatten(),
        layers.Dense(256, activation='relu'),
        layers.Dropout(dropout_rate),
        layers.Dense(num_classes, activation='softmax')
    ])
    print(model.summary())
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

def build_custom_cnn(input_shape=INPUT_SHAPE, num_classes=NUM_CLASSES,
                       conv_dropout_rate=0.25, dense_dropout_rate=0.5):
    inputs = layers.Input(shape=input_shape)

    x = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    x = layers.Conv2D(64, (3, 3), activation='relu')(x)
    x = layers.MaxPooling2D(pool_size=(2, 2))(x)
    x = layers.Dropout(conv_dropout_rate)(x)

    x = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(x)
    x = layers.Conv2D(128, (3, 3), activation='relu')(x)
    x = layers.MaxPooling2D(pool_size=(2, 2))(x)
    x = layers.Dropout(conv_dropout_rate)(x)

    x = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(x)
    x = layers.Conv2D(256, (3, 3), activation='relu')(x)
    x = layers.MaxPooling2D(pool_size=(2, 2))(x)
    x = layers.Dropout(conv_dropout_rate)(x)

    x = layers.Flatten()(x)
    x = layers.Dense(512, activation='relu')(x)
    x = layers.Dropout(dense_dropout_rate)(x)
    x = layers.Dense(256, activation='relu')(x)
    x = layers.Dropout(dense_dropout_rate)(x)
    outputs = layers.Dense(num_classes, activation='softmax')(x)

    model = models.Model(inputs, outputs)
    print(model.summary())
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

models_to_train = {
    "MobileNetV2_1": build_mobilenetv2(dropout_rate=0.2),
    "MobileNetV2_2": build_mobilenetv2(dropout_rate=0.3),
    "MobileNetV2_3": build_mobilenetv2(dropout_rate=0.5),
    "VGG-16_1"      : build_vgg16(dropout_rate=0.2),
    "VGG-16_2"      : build_vgg16(dropout_rate=0.3),
    "VGG-16_3"      : build_vgg16(dropout_rate=0.5),
    "Custom_CNN_1" : build_custom_cnn(conv_dropout_rate=0.2, dense_dropout_rate=0.2),
    "Custom_CNN_2" : build_custom_cnn(conv_dropout_rate=0.5, dense_dropout_rate=0.5),
    "Custom_CNN_3" : build_custom_cnn(conv_dropout_rate=0.25, dense_dropout_rate=0.5)
}

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5
9406464/9406464 ━━━━━━━━━━━━━━━━━━━━ 2s 0us/step

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ mobilenetv2_1.00_224            │ (None, 7, 7, 1280)     │     2,257,984 │
│ (Functional)                    │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling2d        │ (None, 1280)           │             0 │
│ (GlobalAveragePooling2D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 1280)           │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 8)              │        10,248 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 2,268,232 (8.65 MB)

 Trainable params: 10,248 (40.03 KB)

 Non-trainable params: 2,257,984 (8.61 MB)

None

Model: "sequential_1"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ mobilenetv2_1.00_224            │ (None, 7, 7, 1280)     │     2,257,984 │
│ (Functional)                    │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling2d_1      │ (None, 1280)           │             0 │
│ (GlobalAveragePooling2D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 1280)           │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 8)              │        10,248 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 2,268,232 (8.65 MB)

%%time

history_dict = {}
summary_metrics = []  # will collect per‑model evaluation

for name, model in models_to_train.items():
    print(f"\n===== Training {name} =====")
    hist = model.fit(
        train_gen,
        epochs=EPOCHS,
        validation_data=val_gen,
        callbacks=[tf.keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)],
    )

    print(f'Training accuracy:{hist.history["accuracy"][-1]:.2f} Validation accuracy:{hist.history["val_accuracy"][-1]:.2f}')

    # Save formats
    model.save_weights(f"{name}.weights.h5")
    model.save(f"{name}_model.keras")
    with open(f"{name}_model.json", "w", encoding="utf-8") as jf:
        jf.write(model.to_json())

    history_dict[name] = hist.history

    # -------- Evaluation on TEST set --------
    test_gen.reset()
    preds_test = model.predict(test_gen, verbose=0)
    y_pred_test = np.argmax(preds_test, axis=1)
    y_true_test = test_gen.classes

    acc = accuracy_score(y_true_test, y_pred_test)
    prec, rec, f1, _ = precision_recall_fscore_support(
        y_true_test, y_pred_test, average="weighted", zero_division=0
    )
    summary_metrics.append({
        "model": name,
        "accuracy": acc,
        "precision": prec,
        "recall": rec,
        "f1": f1,
    })

    labels = list(train_gen.class_indices.keys())
    print("TEST Confusion Matrix:\n", confusion_matrix(y_true_test, y_pred_test))
    print("TEST Classification Report:\n", classification_report(y_true_test, y_pred_test, target_names=labels))

    # Visualize 10 sample predictions from test set
    class_map = {v: k for k, v in train_gen.class_indices.items()}
    sample_x, sample_y = next(test_gen)
    sample_preds = model.predict(sample_x)
    sample_classes = np.argmax(sample_preds, axis=1)
    true_classes = np.argmax(sample_y, axis=1)

    plt.figure(figsize=(14, 8))
    for i in range(min(10, len(sample_x))):
        plt.subplot(2, 5, i + 1)
        plt.imshow(sample_x[i])
        plt.axis("off")
        pred_label = class_map[sample_classes[i]]
        true_label = class_map[true_classes[i]]
        color = "green" if pred_label == true_label else "red"
        plt.title(f"P: {pred_label}\nT: {true_label}", color=color, fontsize=9)
    plt.suptitle(f"Test‑set predictions by {name}")
    plt.tight_layout()
    plt.show()

===== Training MobileNetV2_1 =====
Epoch 1/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 34s 653ms/step - accuracy: 0.2784 - loss: 2.2273 - val_accuracy: 0.5569 - val_loss: 1.2747
Epoch 2/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 591ms/step - accuracy: 0.5611 - loss: 1.2672 - val_accuracy: 0.6824 - val_loss: 0.9245
Epoch 3/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 22s 562ms/step - accuracy: 0.6548 - loss: 0.9931 - val_accuracy: 0.7294 - val_loss: 0.7954
Epoch 4/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 572ms/step - accuracy: 0.7130 - loss: 0.8562 - val_accuracy: 0.7569 - val_loss: 0.7312
Epoch 5/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 575ms/step - accuracy: 0.6847 - loss: 0.8732 - val_accuracy: 0.7765 - val_loss: 0.6636
Epoch 6/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 612ms/step - accuracy: 0.7199 - loss: 0.7917 - val_accuracy: 0.8235 - val_loss: 0.5983
Epoch 7/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 575ms/step - accuracy: 0.7453 - loss: 0.6951 - val_accuracy: 0.8039 - val_loss: 0.5540
Epoch 8/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 574ms/step - accuracy: 0.7692 - loss: 0.7122 - val_accuracy: 0.8627 - val_loss: 0.5035
Epoch 9/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 589ms/step - accuracy: 0.7825 - loss: 0.6220 - val_accuracy: 0.8235 - val_loss: 0.5301
Epoch 10/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 22s 548ms/step - accuracy: 0.7940 - loss: 0.6266 - val_accuracy: 0.8353 - val_loss: 0.4891
Epoch 11/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 574ms/step - accuracy: 0.7860 - loss: 0.6173 - val_accuracy: 0.8314 - val_loss: 0.5161
Epoch 12/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 561ms/step - accuracy: 0.7812 - loss: 0.6504 - val_accuracy: 0.8196 - val_loss: 0.5245
Epoch 13/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 25s 610ms/step - accuracy: 0.8158 - loss: 0.5413 - val_accuracy: 0.8471 - val_loss: 0.4823
Epoch 14/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 561ms/step - accuracy: 0.8228 - loss: 0.5091 - val_accuracy: 0.8392 - val_loss: 0.4814
Epoch 15/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 576ms/step - accuracy: 0.8021 - loss: 0.5786 - val_accuracy: 0.8392 - val_loss: 0.4532
Epoch 16/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 577ms/step - accuracy: 0.8308 - loss: 0.5072 - val_accuracy: 0.8627 - val_loss: 0.4172
Epoch 17/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 590ms/step - accuracy: 0.8512 - loss: 0.4447 - val_accuracy: 0.8588 - val_loss: 0.4265
Epoch 18/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 568ms/step - accuracy: 0.8181 - loss: 0.5323 - val_accuracy: 0.8588 - val_loss: 0.4748
Epoch 19/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 43s 608ms/step - accuracy: 0.8279 - loss: 0.5082 - val_accuracy: 0.8902 - val_loss: 0.3902
Epoch 20/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 584ms/step - accuracy: 0.8310 - loss: 0.4859 - val_accuracy: 0.8706 - val_loss: 0.3846
Epoch 21/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 613ms/step - accuracy: 0.8156 - loss: 0.5077 - val_accuracy: 0.8549 - val_loss: 0.3790
Epoch 22/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 580ms/step - accuracy: 0.8345 - loss: 0.4920 - val_accuracy: 0.8980 - val_loss: 0.3471
Epoch 23/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 579ms/step - accuracy: 0.8567 - loss: 0.4498 - val_accuracy: 0.8824 - val_loss: 0.3264
Epoch 24/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 585ms/step - accuracy: 0.8490 - loss: 0.4408 - val_accuracy: 0.8824 - val_loss: 0.3449
Epoch 25/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 574ms/step - accuracy: 0.8380 - loss: 0.4657 - val_accuracy: 0.8824 - val_loss: 0.3650
Epoch 26/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 587ms/step - accuracy: 0.8502 - loss: 0.4604 - val_accuracy: 0.9137 - val_loss: 0.2938
Epoch 27/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 585ms/step - accuracy: 0.8482 - loss: 0.4037 - val_accuracy: 0.8863 - val_loss: 0.3354
Epoch 28/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 584ms/step - accuracy: 0.8636 - loss: 0.4102 - val_accuracy: 0.8980 - val_loss: 0.3255
Epoch 29/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 585ms/step - accuracy: 0.8697 - loss: 0.4147 - val_accuracy: 0.9137 - val_loss: 0.2956
Training accuracy:0.86 Validation accuracy:0.91
TEST Confusion Matrix:
 [[ 14   5   1   1   0   3   3   2]
 [  1  24   1   0   0   1   0   1]
 [  3   0  25   7   1   0   0   3]
 [  1   0   4  27   0   0   0   0]
 [  0   0   0   0  18   3   2   3]
 [  0   0   0   1   1  17   1   3]
 [  1   3   0   0   0   5  23   1]
 [  1   0   2   1   1   2   1 101]]
TEST Classification Report:
                 precision    recall  f1-score   support

   bumper_dent       0.67      0.48      0.56        29
bumper_scratch       0.75      0.86      0.80        28
     door_dent       0.76      0.64      0.69        39
  door_scratch       0.73      0.84      0.78        32
 glass_shatter       0.86      0.69      0.77        26
     head_lamp       0.55      0.74      0.63        23
     tail_lamp       0.77      0.70      0.73        33
       unknown       0.89      0.93      0.91       109

      accuracy                           0.78       319
     macro avg       0.75      0.73      0.73       319
  weighted avg       0.78      0.78      0.78       319

1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step

===== Training MobileNetV2_2 =====
Epoch 1/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 30s 656ms/step - accuracy: 0.3297 - loss: 2.1408 - val_accuracy: 0.5451 - val_loss: 1.2526
Epoch 2/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 593ms/step - accuracy: 0.5548 - loss: 1.3011 - val_accuracy: 0.7020 - val_loss: 0.9026
Epoch 3/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 25s 636ms/step - accuracy: 0.6252 - loss: 1.0852 - val_accuracy: 0.7412 - val_loss: 0.8402
Epoch 4/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 595ms/step - accuracy: 0.6663 - loss: 0.9486 - val_accuracy: 0.8000 - val_loss: 0.6620
Epoch 5/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 593ms/step - accuracy: 0.7113 - loss: 0.8492 - val_accuracy: 0.7961 - val_loss: 0.6688
Epoch 6/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 596ms/step - accuracy: 0.7219 - loss: 0.8140 - val_accuracy: 0.7843 - val_loss: 0.6470
Epoch 7/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 597ms/step - accuracy: 0.7358 - loss: 0.7849 - val_accuracy: 0.8118 - val_loss: 0.5712
Epoch 8/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 588ms/step - accuracy: 0.7494 - loss: 0.7472 - val_accuracy: 0.8078 - val_loss: 0.5924
Epoch 9/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 23s 580ms/step - accuracy: 0.7638 - loss: 0.6690 - val_accuracy: 0.8431 - val_loss: 0.4968
Epoch 10/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 44s 662ms/step - accuracy: 0.7999 - loss: 0.6482 - val_accuracy: 0.8235 - val_loss: 0.5068
Epoch 11/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 39s 593ms/step - accuracy: 0.7622 - loss: 0.7037 - val_accuracy: 0.8745 - val_loss: 0.4378
Epoch 12/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 594ms/step - accuracy: 0.7895 - loss: 0.6650 - val_accuracy: 0.7922 - val_loss: 0.5973
Epoch 13/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 593ms/step - accuracy: 0.7981 - loss: 0.5842 - val_accuracy: 0.8392 - val_loss: 0.4440
Epoch 14/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 600ms/step - accuracy: 0.7897 - loss: 0.6206 - val_accuracy: 0.8353 - val_loss: 0.4592
Training accuracy:0.79 Validation accuracy:0.84
TEST Confusion Matrix:
 [[ 15   6   0   0   0   1   4   3]
 [  2  20   1   0   1   2   0   2]
 [  1   1  24   7   1   0   0   5]
 [  0   0   4  26   0   0   1   1]
 [  0   0   0   0  21   0   2   3]
 [  0   0   0   1   1  17   2   2]
 [  1   2   0   0   0   3  24   3]
 [  0   0   3   0   2   0   1 103]]
TEST Classification Report:
                 precision    recall  f1-score   support

   bumper_dent       0.79      0.52      0.62        29
bumper_scratch       0.69      0.71      0.70        28
     door_dent       0.75      0.62      0.68        39
  door_scratch       0.76      0.81      0.79        32
 glass_shatter       0.81      0.81      0.81        26
     head_lamp       0.74      0.74      0.74        23
     tail_lamp       0.71      0.73      0.72        33
       unknown       0.84      0.94      0.89       109

      accuracy                           0.78       319
     macro avg       0.76      0.73      0.74       319
  weighted avg       0.78      0.78      0.78       319

1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 1s/step

===== Training MobileNetV2_3 =====
Epoch 1/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 33s 701ms/step - accuracy: 0.2488 - loss: 2.4107 - val_accuracy: 0.5412 - val_loss: 1.2359
Epoch 2/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 25s 631ms/step - accuracy: 0.4628 - loss: 1.6466 - val_accuracy: 0.6745 - val_loss: 0.9739
Epoch 3/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 622ms/step - accuracy: 0.5774 - loss: 1.2211 - val_accuracy: 0.7098 - val_loss: 0.9121
Epoch 4/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 606ms/step - accuracy: 0.6147 - loss: 1.1421 - val_accuracy: 0.7255 - val_loss: 0.8045
Epoch 5/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 610ms/step - accuracy: 0.6685 - loss: 1.0134 - val_accuracy: 0.8039 - val_loss: 0.7126
Epoch 6/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 605ms/step - accuracy: 0.6564 - loss: 1.0005 - val_accuracy: 0.7725 - val_loss: 0.7087
Epoch 7/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 606ms/step - accuracy: 0.6971 - loss: 0.9182 - val_accuracy: 0.7608 - val_loss: 0.6880
Epoch 8/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 606ms/step - accuracy: 0.7022 - loss: 0.8819 - val_accuracy: 0.8039 - val_loss: 0.6103
Epoch 9/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 25s 626ms/step - accuracy: 0.6761 - loss: 0.9241 - val_accuracy: 0.8078 - val_loss: 0.5643
Epoch 10/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 25s 639ms/step - accuracy: 0.7472 - loss: 0.8033 - val_accuracy: 0.7804 - val_loss: 0.6575
Epoch 11/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 616ms/step - accuracy: 0.7069 - loss: 0.8141 - val_accuracy: 0.8118 - val_loss: 0.5712
Epoch 12/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 597ms/step - accuracy: 0.7521 - loss: 0.7242 - val_accuracy: 0.8235 - val_loss: 0.5614
Epoch 13/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 590ms/step - accuracy: 0.7437 - loss: 0.7355 - val_accuracy: 0.8431 - val_loss: 0.4852
Epoch 14/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 582ms/step - accuracy: 0.7375 - loss: 0.7413 - val_accuracy: 0.8588 - val_loss: 0.4563
Epoch 15/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 599ms/step - accuracy: 0.7483 - loss: 0.7398 - val_accuracy: 0.8549 - val_loss: 0.5155
Epoch 16/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 25s 628ms/step - accuracy: 0.7781 - loss: 0.6795 - val_accuracy: 0.8510 - val_loss: 0.4934
Epoch 17/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 641ms/step - accuracy: 0.7545 - loss: 0.7193 - val_accuracy: 0.8510 - val_loss: 0.4496
Epoch 18/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 39s 597ms/step - accuracy: 0.7672 - loss: 0.6980 - val_accuracy: 0.8196 - val_loss: 0.5150
Epoch 19/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 598ms/step - accuracy: 0.7769 - loss: 0.6363 - val_accuracy: 0.8353 - val_loss: 0.4651
Epoch 20/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 24s 592ms/step - accuracy: 0.7579 - loss: 0.6666 - val_accuracy: 0.8902 - val_loss: 0.3952
Epoch 21/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 618ms/step - accuracy: 0.7800 - loss: 0.6520 - val_accuracy: 0.8118 - val_loss: 0.5442
Epoch 22/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 25s 628ms/step - accuracy: 0.7481 - loss: 0.6887 - val_accuracy: 0.8549 - val_loss: 0.4000
Epoch 23/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 638ms/step - accuracy: 0.7597 - loss: 0.6927 - val_accuracy: 0.8588 - val_loss: 0.4238
Training accuracy:0.76 Validation accuracy:0.86
TEST Confusion Matrix:
 [[ 15   7   0   0   0   1   2   4]
 [  0  23   1   0   0   1   1   2]
 [  2   1  25   5   0   0   0   6]
 [  1   0   5  23   0   0   1   2]
 [  0   0   0   0  22   0   1   3]
 [  0   0   0   1   0  17   1   4]
 [  1   2   0   0   0   2  25   3]
 [  0   0   2   0   2   1   2 102]]
TEST Classification Report:
                 precision    recall  f1-score   support

   bumper_dent       0.79      0.52      0.62        29
bumper_scratch       0.70      0.82      0.75        28
     door_dent       0.76      0.64      0.69        39
  door_scratch       0.79      0.72      0.75        32
 glass_shatter       0.92      0.85      0.88        26
     head_lamp       0.77      0.74      0.76        23
     tail_lamp       0.76      0.76      0.76        33
       unknown       0.81      0.94      0.87       109

      accuracy                           0.79       319
     macro avg       0.79      0.75      0.76       319
  weighted avg       0.79      0.79      0.78       319

1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 1s/step

===== Training VGG-16_1 =====
Epoch 1/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 31s 743ms/step - accuracy: 0.3426 - loss: 3.2862 - val_accuracy: 0.5961 - val_loss: 1.1832
Epoch 2/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 696ms/step - accuracy: 0.5760 - loss: 1.1904 - val_accuracy: 0.6784 - val_loss: 1.0194
Epoch 3/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 713ms/step - accuracy: 0.6295 - loss: 1.0605 - val_accuracy: 0.6118 - val_loss: 1.0791
Epoch 4/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 710ms/step - accuracy: 0.6442 - loss: 1.0759 - val_accuracy: 0.7373 - val_loss: 0.8712
Epoch 5/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 691ms/step - accuracy: 0.6660 - loss: 0.9742 - val_accuracy: 0.7216 - val_loss: 0.8605
Epoch 6/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 688ms/step - accuracy: 0.6726 - loss: 0.9372 - val_accuracy: 0.7725 - val_loss: 0.7258
Epoch 7/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 698ms/step - accuracy: 0.7287 - loss: 0.8308 - val_accuracy: 0.7686 - val_loss: 0.6928
Epoch 8/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 27s 688ms/step - accuracy: 0.7066 - loss: 0.9176 - val_accuracy: 0.7765 - val_loss: 0.7557
Epoch 9/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 715ms/step - accuracy: 0.7070 - loss: 0.8020 - val_accuracy: 0.7961 - val_loss: 0.6490
Epoch 10/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 707ms/step - accuracy: 0.7265 - loss: 0.7646 - val_accuracy: 0.7647 - val_loss: 0.7133
Epoch 11/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 684ms/step - accuracy: 0.7557 - loss: 0.7013 - val_accuracy: 0.7490 - val_loss: 0.7472
Epoch 12/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 714ms/step - accuracy: 0.7194 - loss: 0.7931 - val_accuracy: 0.7882 - val_loss: 0.6050
Epoch 13/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 706ms/step - accuracy: 0.7363 - loss: 0.7310 - val_accuracy: 0.8000 - val_loss: 0.5585
Epoch 14/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 687ms/step - accuracy: 0.7653 - loss: 0.6623 - val_accuracy: 0.7843 - val_loss: 0.5890
Epoch 15/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 706ms/step - accuracy: 0.7622 - loss: 0.6598 - val_accuracy: 0.8196 - val_loss: 0.5238
Epoch 16/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 33s 828ms/step - accuracy: 0.7445 - loss: 0.7107 - val_accuracy: 0.8196 - val_loss: 0.5244
Epoch 17/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 721ms/step - accuracy: 0.7689 - loss: 0.6616 - val_accuracy: 0.8078 - val_loss: 0.5706
Epoch 18/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 701ms/step - accuracy: 0.7813 - loss: 0.6210 - val_accuracy: 0.8471 - val_loss: 0.4994
Epoch 19/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 714ms/step - accuracy: 0.7820 - loss: 0.6125 - val_accuracy: 0.8627 - val_loss: 0.4063
Epoch 20/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 709ms/step - accuracy: 0.8121 - loss: 0.5781 - val_accuracy: 0.8275 - val_loss: 0.5352
Epoch 21/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 691ms/step - accuracy: 0.7450 - loss: 0.6837 - val_accuracy: 0.8353 - val_loss: 0.5032
Epoch 22/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 720ms/step - accuracy: 0.7534 - loss: 0.6823 - val_accuracy: 0.8392 - val_loss: 0.5006
Training accuracy:0.75 Validation accuracy:0.84
TEST Confusion Matrix:
 [[17  7  0  0  0  0  4  1]
 [ 1 27  0  0  0  0  0  0]
 [ 1  0 27  6  3  0  0  2]
 [ 1  5 11 14  0  0  0  1]
 [ 0  0  0  0 19  1  1  5]
 [ 2  0  0  0  1 17  1  2]
 [ 2  3  0  0  1  4 22  1]
 [ 3  1  5  0  2  8  0 90]]
TEST Classification Report:
                 precision    recall  f1-score   support

   bumper_dent       0.63      0.59      0.61        29
bumper_scratch       0.63      0.96      0.76        28
     door_dent       0.63      0.69      0.66        39
  door_scratch       0.70      0.44      0.54        32
 glass_shatter       0.73      0.73      0.73        26
     head_lamp       0.57      0.74      0.64        23
     tail_lamp       0.79      0.67      0.72        33
       unknown       0.88      0.83      0.85       109

      accuracy                           0.73       319
     macro avg       0.69      0.71      0.69       319
  weighted avg       0.74      0.73      0.73       319

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step

===== Training VGG-16_2 =====
Epoch 1/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 31s 748ms/step - accuracy: 0.3013 - loss: 4.5882 - val_accuracy: 0.5333 - val_loss: 1.2680
Epoch 2/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 723ms/step - accuracy: 0.5227 - loss: 1.3256 - val_accuracy: 0.5412 - val_loss: 1.2216
Epoch 3/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 726ms/step - accuracy: 0.5392 - loss: 1.2562 - val_accuracy: 0.6667 - val_loss: 1.0580
Epoch 4/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 696ms/step - accuracy: 0.6132 - loss: 1.1011 - val_accuracy: 0.6510 - val_loss: 1.0607
Epoch 5/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 725ms/step - accuracy: 0.6447 - loss: 1.0884 - val_accuracy: 0.7059 - val_loss: 0.9341
Epoch 6/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 739ms/step - accuracy: 0.6077 - loss: 1.0959 - val_accuracy: 0.7412 - val_loss: 0.8704
Epoch 7/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 39s 693ms/step - accuracy: 0.6362 - loss: 1.0419 - val_accuracy: 0.7373 - val_loss: 0.8110
Epoch 8/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 720ms/step - accuracy: 0.6178 - loss: 1.0153 - val_accuracy: 0.6941 - val_loss: 0.8821
Epoch 9/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 710ms/step - accuracy: 0.6447 - loss: 0.9705 - val_accuracy: 0.7020 - val_loss: 0.8233
Epoch 10/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 698ms/step - accuracy: 0.6643 - loss: 0.8898 - val_accuracy: 0.7451 - val_loss: 0.7471
Epoch 11/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 722ms/step - accuracy: 0.7006 - loss: 0.8376 - val_accuracy: 0.7608 - val_loss: 0.7028
Epoch 12/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 736ms/step - accuracy: 0.6935 - loss: 0.8798 - val_accuracy: 0.7882 - val_loss: 0.6579
Epoch 13/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 712ms/step - accuracy: 0.7089 - loss: 0.8633 - val_accuracy: 0.7882 - val_loss: 0.6541
Epoch 14/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 699ms/step - accuracy: 0.7023 - loss: 0.8383 - val_accuracy: 0.7608 - val_loss: 0.7717
Epoch 15/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 706ms/step - accuracy: 0.6575 - loss: 0.9651 - val_accuracy: 0.7961 - val_loss: 0.6170
Epoch 16/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 720ms/step - accuracy: 0.7173 - loss: 0.7779 - val_accuracy: 0.7686 - val_loss: 0.6630
Epoch 17/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 692ms/step - accuracy: 0.7170 - loss: 0.8309 - val_accuracy: 0.8118 - val_loss: 0.6147
Epoch 18/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 30s 745ms/step - accuracy: 0.7273 - loss: 0.7643 - val_accuracy: 0.8353 - val_loss: 0.5829
Epoch 19/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 39s 692ms/step - accuracy: 0.7156 - loss: 0.8177 - val_accuracy: 0.8157 - val_loss: 0.5814
Epoch 20/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 716ms/step - accuracy: 0.7493 - loss: 0.7061 - val_accuracy: 0.8392 - val_loss: 0.5392
Epoch 21/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 715ms/step - accuracy: 0.7132 - loss: 0.7847 - val_accuracy: 0.8196 - val_loss: 0.5298
Epoch 22/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 691ms/step - accuracy: 0.7353 - loss: 0.7648 - val_accuracy: 0.7725 - val_loss: 0.6020
Epoch 23/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 693ms/step - accuracy: 0.7536 - loss: 0.7038 - val_accuracy: 0.8039 - val_loss: 0.5368
Epoch 24/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 43s 746ms/step - accuracy: 0.7340 - loss: 0.7011 - val_accuracy: 0.7922 - val_loss: 0.5515
Training accuracy:0.75 Validation accuracy:0.79
TEST Confusion Matrix:
 [[18  1  1  0  0  1  4  4]
 [ 0 23  0  0  0  3  0  2]
 [ 1  0 26  2  2  0  0  8]
 [ 0  2 18 10  0  0  0  2]
 [ 0  0  0  0 16  2  2  6]
 [ 1  0  0  0  0 19  1  2]
 [ 3  0  1  0  0  4 24  1]
 [ 0  0  3  0  1  5  1 99]]
TEST Classification Report:
                 precision    recall  f1-score   support

   bumper_dent       0.78      0.62      0.69        29
bumper_scratch       0.88      0.82      0.85        28
     door_dent       0.53      0.67      0.59        39
  door_scratch       0.83      0.31      0.45        32
 glass_shatter       0.84      0.62      0.71        26
     head_lamp       0.56      0.83      0.67        23
     tail_lamp       0.75      0.73      0.74        33
       unknown       0.80      0.91      0.85       109

      accuracy                           0.74       319
     macro avg       0.75      0.69      0.69       319
  weighted avg       0.76      0.74      0.73       319

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 492ms/step

===== Training VGG-16_3 =====
Epoch 1/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 30s 731ms/step - accuracy: 0.2984 - loss: 3.7053 - val_accuracy: 0.4353 - val_loss: 1.5076
Epoch 2/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 699ms/step - accuracy: 0.4410 - loss: 1.5623 - val_accuracy: 0.4980 - val_loss: 1.3504
Epoch 3/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 34s 856ms/step - accuracy: 0.4191 - loss: 1.5693 - val_accuracy: 0.5373 - val_loss: 1.3389
Epoch 4/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 35s 709ms/step - accuracy: 0.4362 - loss: 1.4782 - val_accuracy: 0.5137 - val_loss: 1.2574
Epoch 5/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 735ms/step - accuracy: 0.4809 - loss: 1.3507 - val_accuracy: 0.5843 - val_loss: 1.2677
Epoch 6/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 713ms/step - accuracy: 0.4827 - loss: 1.3873 - val_accuracy: 0.5843 - val_loss: 1.1704
Epoch 7/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 727ms/step - accuracy: 0.5067 - loss: 1.2891 - val_accuracy: 0.6000 - val_loss: 1.1804
Epoch 8/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 723ms/step - accuracy: 0.4673 - loss: 1.3664 - val_accuracy: 0.6353 - val_loss: 1.0968
Epoch 9/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 724ms/step - accuracy: 0.4978 - loss: 1.3043 - val_accuracy: 0.6706 - val_loss: 1.1239
Epoch 10/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 698ms/step - accuracy: 0.4952 - loss: 1.2992 - val_accuracy: 0.6196 - val_loss: 1.1090
Epoch 11/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 713ms/step - accuracy: 0.5105 - loss: 1.3083 - val_accuracy: 0.6902 - val_loss: 1.0328
Epoch 12/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 708ms/step - accuracy: 0.5129 - loss: 1.2656 - val_accuracy: 0.6824 - val_loss: 1.0967
Epoch 13/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 720ms/step - accuracy: 0.5293 - loss: 1.2728 - val_accuracy: 0.7059 - val_loss: 0.9624
Epoch 14/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 28s 706ms/step - accuracy: 0.5403 - loss: 1.2136 - val_accuracy: 0.7176 - val_loss: 0.9478
Epoch 15/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 701ms/step - accuracy: 0.5440 - loss: 1.2139 - val_accuracy: 0.6667 - val_loss: 1.0105
Epoch 16/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 30s 755ms/step - accuracy: 0.5304 - loss: 1.2376 - val_accuracy: 0.7020 - val_loss: 0.9012
Epoch 17/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 40s 729ms/step - accuracy: 0.5498 - loss: 1.1659 - val_accuracy: 0.7059 - val_loss: 0.9132
Epoch 18/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 725ms/step - accuracy: 0.5098 - loss: 1.2384 - val_accuracy: 0.7255 - val_loss: 0.8490
Epoch 19/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 715ms/step - accuracy: 0.5663 - loss: 1.1341 - val_accuracy: 0.7529 - val_loss: 0.8162
Epoch 20/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 744ms/step - accuracy: 0.5689 - loss: 1.1163 - val_accuracy: 0.7216 - val_loss: 0.8047
Epoch 21/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 41s 752ms/step - accuracy: 0.5598 - loss: 1.1472 - val_accuracy: 0.7020 - val_loss: 0.9103
Epoch 22/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 39s 703ms/step - accuracy: 0.5777 - loss: 1.1148 - val_accuracy: 0.7451 - val_loss: 0.8286
Epoch 23/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 29s 726ms/step - accuracy: 0.5592 - loss: 1.1275 - val_accuracy: 0.7373 - val_loss: 0.8357
Training accuracy:0.57 Validation accuracy:0.74
TEST Confusion Matrix:
 [[ 8  9  3  0  2  2  3  2]
 [ 3 16  2  0  1  2  1  3]
 [ 2  0 27  1  4  0  0  5]
 [ 1  1 26  2  0  0  0  2]
 [ 0  0  0  0 19  1  0  6]
 [ 0  0  0  0  2 15  0  6]
 [ 1  1  1  0  3  5 20  2]
 [ 0  1  3  0  0  4  2 99]]
TEST Classification Report:
                 precision    recall  f1-score   support

   bumper_dent       0.53      0.28      0.36        29
bumper_scratch       0.57      0.57      0.57        28
     door_dent       0.44      0.69      0.53        39
  door_scratch       0.67      0.06      0.11        32
 glass_shatter       0.61      0.73      0.67        26
     head_lamp       0.52      0.65      0.58        23
     tail_lamp       0.77      0.61      0.68        33
       unknown       0.79      0.91      0.85       109

      accuracy                           0.65       319
     macro avg       0.61      0.56      0.54       319
  weighted avg       0.66      0.65      0.62       319

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step

===== Training Custom_CNN_1 =====
Epoch 1/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 48s 1s/step - accuracy: 0.2917 - loss: 2.8728 - val_accuracy: 0.3725 - val_loss: 1.8935
Epoch 2/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 81s 1s/step - accuracy: 0.3625 - loss: 1.8905 - val_accuracy: 0.3725 - val_loss: 1.8322
Epoch 3/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 81s 1s/step - accuracy: 0.3304 - loss: 1.8575 - val_accuracy: 0.3725 - val_loss: 1.8439
Epoch 4/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 83s 1s/step - accuracy: 0.3568 - loss: 1.8241 - val_accuracy: 0.3725 - val_loss: 1.7760
Epoch 5/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 82s 1s/step - accuracy: 0.3330 - loss: 1.8575 - val_accuracy: 0.4039 - val_loss: 1.7082
Epoch 6/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 1s/step - accuracy: 0.3569 - loss: 1.7522 - val_accuracy: 0.4078 - val_loss: 1.6350
Epoch 7/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 1s/step - accuracy: 0.3906 - loss: 1.6961 - val_accuracy: 0.4314 - val_loss: 1.6319
Epoch 8/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 82s 1s/step - accuracy: 0.3894 - loss: 1.7214 - val_accuracy: 0.4078 - val_loss: 1.6217
Epoch 9/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 83s 1s/step - accuracy: 0.3896 - loss: 1.6934 - val_accuracy: 0.4118 - val_loss: 1.6743
Epoch 10/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 81s 1s/step - accuracy: 0.4022 - loss: 1.6699 - val_accuracy: 0.3882 - val_loss: 1.6262
Epoch 11/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 83s 1s/step - accuracy: 0.3819 - loss: 1.6829 - val_accuracy: 0.4039 - val_loss: 1.6381
Training accuracy:0.40 Validation accuracy:0.40
TEST Confusion Matrix:
 [[  0   2  18   3   0   0   0   6]
 [  0   2   7   1   0   0   0  18]
 [  0   2  15   5   0   0   0  17]
 [  0   2  17   4   0   0   0   9]
 [  0   1   3   1   0   0   0  21]
 [  0   1   2   0   0   0   0  20]
 [  0   1  13   1   0   0   0  18]
 [  0   0   3   0   0   0   0 106]]
TEST Classification Report:
                 precision    recall  f1-score   support

   bumper_dent       0.00      0.00      0.00        29
bumper_scratch       0.18      0.07      0.10        28
     door_dent       0.19      0.38      0.26        39
  door_scratch       0.27      0.12      0.17        32
 glass_shatter       0.00      0.00      0.00        26
     head_lamp       0.00      0.00      0.00        23
     tail_lamp       0.00      0.00      0.00        33
       unknown       0.49      0.97      0.65       109

      accuracy                           0.40       319
     macro avg       0.14      0.19      0.15       319
  weighted avg       0.23      0.40      0.28       319

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 202ms/step

===== Training Custom_CNN_2 =====
Epoch 1/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 49s 1s/step - accuracy: 0.2748 - loss: 4.5684 - val_accuracy: 0.3725 - val_loss: 1.8889
Epoch 2/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 46s 1s/step - accuracy: 0.3119 - loss: 2.0073 - val_accuracy: 0.3725 - val_loss: 1.8796
Epoch 3/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 80s 1s/step - accuracy: 0.3395 - loss: 1.9350 - val_accuracy: 0.3725 - val_loss: 1.8819
Epoch 4/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 44s 1s/step - accuracy: 0.3315 - loss: 1.8766 - val_accuracy: 0.3725 - val_loss: 1.7634
Epoch 5/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 45s 1s/step - accuracy: 0.3605 - loss: 1.8311 - val_accuracy: 0.3725 - val_loss: 1.7289
Epoch 6/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 43s 1s/step - accuracy: 0.3347 - loss: 1.8296 - val_accuracy: 0.3725 - val_loss: 1.7334
Epoch 7/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 42s 1s/step - accuracy: 0.3329 - loss: 1.7946 - val_accuracy: 0.4078 - val_loss: 1.8382
Epoch 8/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 84s 1s/step - accuracy: 0.3548 - loss: 1.7761 - val_accuracy: 0.4118 - val_loss: 1.7098
Epoch 9/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 43s 1s/step - accuracy: 0.3902 - loss: 1.7492 - val_accuracy: 0.4157 - val_loss: 1.7188
Epoch 10/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 83s 1s/step - accuracy: 0.3794 - loss: 1.7540 - val_accuracy: 0.4039 - val_loss: 1.6473
Epoch 11/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 44s 1s/step - accuracy: 0.4070 - loss: 1.7199 - val_accuracy: 0.3882 - val_loss: 1.7429
Epoch 12/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 82s 1s/step - accuracy: 0.4068 - loss: 1.6567 - val_accuracy: 0.3882 - val_loss: 1.6669
Epoch 13/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 82s 1s/step - accuracy: 0.4015 - loss: 1.6777 - val_accuracy: 0.4000 - val_loss: 1.6705
Training accuracy:0.39 Validation accuracy:0.40
TEST Confusion Matrix:
 [[  0   0  17   2   0   0   0  10]
 [  0   0   7   1   0   0   0  20]
 [  0   0  14   2   0   0   0  23]
 [  0   0  12   3   0   0   0  17]
 [  0   0   1   0   0   0   0  25]
 [  0   0   2   0   0   0   0  21]
 [  0   0   8   0   0   0   0  25]
 [  0   0   3   0   0   0   0 106]]
TEST Classification Report:
                 precision    recall  f1-score   support

   bumper_dent       0.00      0.00      0.00        29
bumper_scratch       0.00      0.00      0.00        28
     door_dent       0.22      0.36      0.27        39
  door_scratch       0.38      0.09      0.15        32
 glass_shatter       0.00      0.00      0.00        26
     head_lamp       0.00      0.00      0.00        23
     tail_lamp       0.00      0.00      0.00        33
       unknown       0.43      0.97      0.60       109

      accuracy                           0.39       319
     macro avg       0.13      0.18      0.13       319
  weighted avg       0.21      0.39      0.25       319

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 224ms/step

===== Training Custom_CNN_3 =====
Epoch 1/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 48s 1s/step - accuracy: 0.2662 - loss: 3.3282 - val_accuracy: 0.3725 - val_loss: 1.8740
Epoch 2/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 82s 1s/step - accuracy: 0.3547 - loss: 1.9094 - val_accuracy: 0.3725 - val_loss: 1.7867
Epoch 3/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 81s 1s/step - accuracy: 0.3322 - loss: 1.8869 - val_accuracy: 0.3725 - val_loss: 1.7557
Epoch 4/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 81s 1s/step - accuracy: 0.3481 - loss: 1.8059 - val_accuracy: 0.3765 - val_loss: 1.7130
Epoch 5/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 44s 1s/step - accuracy: 0.3688 - loss: 1.7559 - val_accuracy: 0.4000 - val_loss: 1.6911
Epoch 6/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 44s 1s/step - accuracy: 0.3604 - loss: 1.8173 - val_accuracy: 0.3843 - val_loss: 1.6973
Epoch 7/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 81s 1s/step - accuracy: 0.3952 - loss: 1.7224 - val_accuracy: 0.4275 - val_loss: 1.6093
Epoch 8/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 83s 1s/step - accuracy: 0.3824 - loss: 1.7184 - val_accuracy: 0.4118 - val_loss: 1.6344
Epoch 9/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 43s 1s/step - accuracy: 0.3636 - loss: 1.7593 - val_accuracy: 0.3765 - val_loss: 1.6543
Epoch 10/100
40/40 ━━━━━━━━━━━━━━━━━━━━ 82s 1s/step - accuracy: 0.4136 - loss: 1.6879 - val_accuracy: 0.4078 - val_loss: 1.6255
Training accuracy:0.40 Validation accuracy:0.41
TEST Confusion Matrix:
 [[  0   0  13  11   0   0   0   5]
 [  0   0   6   5   0   0   0  17]
 [  0   0  18   6   0   0   0  15]
 [  0   0  13  12   0   0   0   7]
 [  0   0   3   1   0   0   0  22]
 [  0   0   3   2   0   0   0  18]
 [  0   0  10   7   0   0   0  16]
 [  0   0   5   0   0   0   0 104]]
TEST Classification Report:
                 precision    recall  f1-score   support

   bumper_dent       0.00      0.00      0.00        29
bumper_scratch       0.00      0.00      0.00        28
     door_dent       0.25      0.46      0.33        39
  door_scratch       0.27      0.38      0.32        32
 glass_shatter       0.00      0.00      0.00        26
     head_lamp       0.00      0.00      0.00        23
     tail_lamp       0.00      0.00      0.00        33
       unknown       0.51      0.95      0.66       109

      accuracy                           0.42       319
     macro avg       0.13      0.22      0.16       319
  weighted avg       0.23      0.42      0.30       319

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 209ms/step

CPU times: user 1h 2min 21s, sys: 1min 44s, total: 1h 4min 6s
Wall time: 1h 57min 40s

#  Visualization of training curves
def plot_history(histories):
    for name, history in histories.items():
        epochs_range = range(1, len(history["accuracy"]) + 1)
        #epochs_range = range(1, 41)
        plt.figure(figsize=(12, 4))
        plt.subplot(1, 2, 1)
        plt.plot(epochs_range, history["accuracy"], label="Train")
        plt.plot(epochs_range, history["val_accuracy"], label="Val")
        plt.title(f"{name} Accuracy")
        plt.xlabel("Epoch"); plt.ylabel("Accuracy"); plt.legend()
        plt.subplot(1, 2, 2)
        plt.plot(epochs_range, history["loss"], label="Train")
        plt.plot(epochs_range, history["val_loss"], label="Val")
        plt.title(f"{name} Loss")
        plt.xlabel("Epoch"); plt.ylabel("Loss"); plt.legend()
        plt.tight_layout(); plt.show()

plot_history(history_dict)

#  Summary bar‑chart of model metrics
summary_df = pd.DataFrame(summary_metrics)
print("\nTest‑set summary metrics (ImageSize: 224):\n", summary_df)

# Bar chart of accuracy
plt.figure(figsize=(8, 5))
plt.bar(summary_df["model"], summary_df["accuracy"])
plt.title("Test Accuracy per Model"); plt.ylabel("Accuracy"); plt.ylim(0, 1)
plt.xticks(rotation=90)
plt.xlabel("Model"); plt.tight_layout(); plt.show()

print("\nAll done — models saved and evaluation summary complete!")

Test‑set summary metrics (ImageSize: 224):
            model  accuracy  precision    recall        f1
0  MobileNetV2_1  0.780564   0.783696  0.780564  0.777410
1  MobileNetV2_2  0.783699   0.781330  0.783699  0.778049
2  MobileNetV2_3  0.789969   0.790529  0.789969  0.784744
3       VGG-16_1  0.730408   0.742531  0.730408  0.728402
4       VGG-16_2  0.736677   0.756575  0.736677  0.728331
5       VGG-16_3  0.645768   0.656202  0.645768  0.615236
6   Custom_CNN_1  0.398119   0.234683  0.398119  0.281002
7   Custom_CNN_2  0.385580   0.210999  0.385580  0.251762
8   Custom_CNN_3  0.420063   0.232549  0.420063  0.298757

All done — models saved and evaluation summary complete!

# Find best model by accuracy
best_model_info = max(summary_metrics, key=lambda x: x['accuracy'])
best_model_name = best_model_info['model']
print(f"Best model is {best_model_name} with Test Accuracy {best_model_info['accuracy']:.4f}")

# Get the corresponding trained model
best_model = models_to_train[best_model_name]

# Save the model weights
best_model.save_weights("best_model.weights.h5")

# Save the full model
best_model.save("best_model.keras")

# Save the model architecture (JSON)
with open("best_model.json", "w", encoding="utf-8") as jf:
    jf.write(best_model.to_json())

print("Best model and its components saved: best_model.weights.h5, best_model.keras, and best_model.json")

Best model is MobileNetV2_3 with Test Accuracy 0.7900
Best model and its components saved: best_model.weights.h5, best_model.keras, and best_model.json

Capstone Project¶

Automated Car Damage Detection for Insurance Claims¶

Business problem¶

Objective¶

Dataset¶

Import libraries¶

Global configuration¶

Load data¶

Exploratory Data Analysis (EDA)¶

Data Preparation¶

Train-Test Split¶

Data augmentation¶

Check data¶

Model builders¶

Model Training, Evaluation & Prediction¶

Visualization of Training Curves¶

Summary of Model Metrics¶

Best Model¶

Model Deployment¶

	Unnamed: 0	image	classes
0	0	image/0.jpeg	unknown
1	1	image/1.jpeg	head_lamp
2	2	image/2.jpeg	door_scratch
3	3	image/3.jpeg	head_lamp
4	4	image/4.jpeg	unknown

	image	classes
0	./CarDamage/image/0.jpeg	unknown
1	./CarDamage/image/1.jpeg	head_lamp
2	./CarDamage/image/2.jpeg	door_scratch
3	./CarDamage/image/3.jpeg	head_lamp
4	./CarDamage/image/4.jpeg	unknown